{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "from scipy import misc\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils import shuffle\n",
    "import pickle\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CIFAR10/CIFAR100地址：\n",
    "\n",
    "http://www.cs.toronto.edu/~kriz/cifar.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unpickle(file):\n",
    "    import pickle\n",
    "    with open(file, 'rb') as fo:\n",
    "        dict = pickle.load(fo, encoding='bytes')\n",
    "    return dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 加载标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta=unpickle(\"./cifar-100-python/meta\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([b'fine_label_names', b'coarse_label_names'])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 粗糙标签名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[b'aquatic_mammals',\n",
       " b'fish',\n",
       " b'flowers',\n",
       " b'food_containers',\n",
       " b'fruit_and_vegetables',\n",
       " b'household_electrical_devices',\n",
       " b'household_furniture',\n",
       " b'insects',\n",
       " b'large_carnivores',\n",
       " b'large_man-made_outdoor_things',\n",
       " b'large_natural_outdoor_scenes',\n",
       " b'large_omnivores_and_herbivores',\n",
       " b'medium_mammals',\n",
       " b'non-insect_invertebrates',\n",
       " b'people',\n",
       " b'reptiles',\n",
       " b'small_mammals',\n",
       " b'trees',\n",
       " b'vehicles_1',\n",
       " b'vehicles_2']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta[b'coarse_label_names']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 精细标签名共100个（100个分类）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(meta[b'fine_label_names'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 粗糙标签共20个分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(meta[b'coarse_label_names'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 加载训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainset=unpickle(\"./cifar-100-python/train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([b'filenames', b'batch_label', b'fine_labels', b'coarse_labels', b'data'])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 3072)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset[b'data'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainset_x=trainset[b'data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_trainset=len(trainset_x)\n",
    "n_trainset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_class=len(meta[b'coarse_label_names'])\n",
    "n_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainset_y=trainset[b'coarse_labels']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 加载测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "testset=unpickle(\"./cifar-100-python/test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "testset_x=testset[b'data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "testset_y=testset[b'coarse_labels']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 将数据集图像数据转成RGB图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainset_x=trainset_x.reshape(-1,3,32,32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainset_x=np.rollaxis(trainset_x, 1, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 32, 32, 3)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 32, 32, 3)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testset_x=testset_x.reshape(-1,3,32,32)\n",
    "testset_x=np.rollaxis(testset_x, 1, 4)\n",
    "testset_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "b'household_furniture'\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import random\n",
    "\n",
    "index = random.randint(0, len(trainset_x))\n",
    "plt.figure()\n",
    "plt.imshow(trainset_x[index])\n",
    "print(trainset_y[index])\n",
    "print(meta[b'coarse_label_names'][trainset_y[index]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据增强处理函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "import shutil\n",
    "from PIL import Image\n",
    "import sys\n",
    "\n",
    "current_num = 0\n",
    "\n",
    "def CLAHE(img):\n",
    "    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))\n",
    "    cl1 = clahe.apply(img)\n",
    "    return cl1\n",
    "\n",
    "def Histograms_Equalization(img):\n",
    "    equ = cv2.equalizeHist(img)\n",
    "    return equ\n",
    "\n",
    "def make_one_hot(data, num):\n",
    "    return (np.arange(num)==data[:,None]).astype(np.integer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 处理训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All done!50000\n"
     ]
    }
   ],
   "source": [
    "from sklearn.utils import shuffle\n",
    "\n",
    "new_X_train = []\n",
    "new_y_train = []\n",
    "for index in range(len(trainset_x)):\n",
    "    sys.stdout.write(\" {} / {}\\r\".format(index, len(trainset_x)))\n",
    "\n",
    "    img_gray = cv2.cvtColor(trainset_x[index], cv2.COLOR_RGB2GRAY)\n",
    "\n",
    "    new_X_train.append(img_gray.astype('float32') / 255.0)\n",
    "    new_y_train.append(trainset_y[index])\n",
    "    \n",
    "    he_image = Histograms_Equalization(img_gray)\n",
    "    new_X_train.append(he_image.astype('float32') / 255.0)\n",
    "    new_y_train.append(trainset_y[index])\n",
    "    \n",
    "    clahe_img = CLAHE(img_gray)\n",
    "    new_X_train.append(clahe_img.astype('float32') / 255.0)\n",
    "    new_y_train.append(trainset_y[index])\n",
    "    \n",
    "    \n",
    "print(\"All done!\")\n",
    "all_xs = np.expand_dims(new_X_train, 3)\n",
    "all_ys = make_one_hot(np.array(new_y_train), n_class)\n",
    "train_xs, valid_xs, train_ys, valid_ys = train_test_split(\n",
    "    all_xs, all_ys, test_size=0.2, random_state=0)\n",
    "# all_xs, all_ys = shuffle(np.array(new_X_train), np.array(new_y_train), random_state=0)\n",
    "\n",
    "pickle.dump(all_xs, open('all_xs.p', 'wb'))\n",
    "pickle.dump(all_ys, open('all_ys.p', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11a538ef0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = 17245 \n",
    "img_gray = cv2.cvtColor(trainset_x[index], cv2.COLOR_RGB2GRAY)\n",
    "plt.figure()\n",
    "plt.imshow(img_gray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17245\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a2a241c88>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = 17245 \n",
    "print(index)\n",
    "img_gray = cv2.cvtColor(trainset_x[index], cv2.COLOR_RGB2GRAY)\n",
    "# img_gray = Histograms_Equalization(img_gray)\n",
    "img_gray = CLAHE(img_gray)\n",
    "plt.figure()\n",
    "plt.imshow(img_gray)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 处理测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle\n",
    "\n",
    "test_set = []\n",
    "for j in range(len(testset_x)):\n",
    "    img_gray = cv2.cvtColor(testset_x[j], cv2.COLOR_RGB2GRAY)\n",
    "#     img_gray = Histograms_Equalization(img_gray)\n",
    "    img_gray = np.expand_dims(img_gray, 2)\n",
    "    img_gray = img_gray / 255.0\n",
    "    test_set.append(img_gray)\n",
    "\n",
    "test_set = np.array(test_set)\n",
    "y_test = make_one_hot(np.array(testset_y), n_class)\n",
    "\n",
    "def get_batches(Xs, ys, batch_size):\n",
    "    for start in range(0, len(Xs), batch_size):\n",
    "        end = min(start + batch_size, len(Xs))\n",
    "        yield Xs[start:end], ys[start:end]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 构建网络模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import datetime\n",
    "\n",
    "class network(object):\n",
    "    def __init__(self):\n",
    "\n",
    "        self.graph = tf.Graph()\n",
    "        with self.graph.as_default():\n",
    "            self.x = tf.placeholder(tf.float32, shape=[None, 32, 32, 1], name='input_tensor')\n",
    "            self.y = tf.placeholder(tf.float32, shape=[None, n_class], name='labels')\n",
    "            \n",
    "            conv1 = tf.layers.conv2d(self.x, filters=100, kernel_size=5, activation=tf.nn.relu)            \n",
    "            print(\"conv1.shape = \", conv1.get_shape())  # (?, 28, 28, 100)    \n",
    "            self.conv1 = tf.layers.max_pooling2d(conv1, pool_size=2, strides=2)\n",
    "\n",
    "            conv2 = tf.layers.conv2d(self.conv1, filters=150, kernel_size=3, activation=tf.nn.relu)            \n",
    "            print(\"conv2.shape = \", conv2.get_shape())  # (?, 12, 12, 150)     \n",
    "            self.conv2 = tf.layers.max_pooling2d(conv2, pool_size=2, strides=2)\n",
    "            print(\"max_pool conv2.shape = \", self.conv2.get_shape())  # (?, 6, 6, 150)  \n",
    "\n",
    "            conv3 = tf.layers.conv2d(self.conv2, filters=250, kernel_size=3, padding='same', activation=tf.nn.relu)            \n",
    "            print(\"conv3.shape = \", conv3.get_shape())  # (?, 6, 6, 250)  \n",
    "            self.conv3 = tf.layers.max_pooling2d(conv3, pool_size=2, strides=2)\n",
    "            print(\"max_pool conv3.shape = \", self.conv3.get_shape())  # (?, 3, 3, 250)  \n",
    "\n",
    "            self.fc0   = tf.layers.flatten(self.conv3)\n",
    "            print(\"fc0.shape = \", self.fc0.get_shape())  # (?, 2250)  \n",
    "\n",
    "            self.fc1 = tf.layers.dense(self.fc0, units=512, activation=tf.nn.relu)\n",
    "\n",
    "            self.fc2 = tf.layers.dense(self.fc1, units=300, activation=tf.nn.relu)\n",
    "            \n",
    "            self.logits = tf.layers.dense(self.fc2, units=n_class)\n",
    "\n",
    "            with tf.name_scope('loss'):\n",
    "                self.cross_entropy = tf.reduce_mean(\n",
    "                    tf.losses.softmax_cross_entropy(self.y, logits=self.logits))\n",
    "\n",
    "            with tf.name_scope('train_step'):\n",
    "                self.train_step = tf.train.AdamOptimizer(1e-4).minimize(self.cross_entropy)\n",
    "\n",
    "            with tf.name_scope('accuracy'):\n",
    "                correct_prediction = tf.equal(tf.argmax(self.logits,1), tf.argmax(self.y,1))\n",
    "                self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "\n",
    "            self.saver = tf.train.Saver()\n",
    "            self.sess = tf.Session(graph=self.graph)\n",
    "            self.sess.run(tf.global_variables_initializer())\n",
    "\n",
    "            self.save_dir = './session'\n",
    "            if not os.path.isdir(self.save_dir):\n",
    "                os.mkdir(self.save_dir)\n",
    "            self.ckpt = tf.train.get_checkpoint_state(self.save_dir)\n",
    "            if self.ckpt and self.ckpt.model_checkpoint_path:\n",
    "                self.saver.restore(self.sess, tf.train.latest_checkpoint(self.save_dir))\n",
    "                print(\"Successfully loaded:\", tf.train.latest_checkpoint(self.save_dir))\n",
    "            else:\n",
    "                print(\"Could not find old network weights\")\n",
    "\n",
    "    def training(self, xs, labels, ii, epoch_i, batch_i, batch_num):\n",
    "        feed_dict = {\n",
    "            self.x: xs,\n",
    "            self.y: labels\n",
    "        }\n",
    "\n",
    "        _, accuracy, loss = self.sess.run([self.train_step, self.accuracy, self.cross_entropy], feed_dict=feed_dict)\n",
    "        if (ii % 20 == 0):\n",
    "            time_str = datetime.datetime.now().isoformat()\n",
    "            print('Training {}: Epoch {:>3} Batch {:>4}/{}   train_loss = {:.5f}'.format(\n",
    "                    time_str,\n",
    "                    epoch_i,\n",
    "                    batch_i,\n",
    "                    batch_num,\n",
    "                    loss))\n",
    "\n",
    "        return accuracy, loss\n",
    "    \n",
    "    def testing(self, xs, labels, ii, epoch_i, batch_i, batch_num):\n",
    "        feed_dict = {\n",
    "            self.x: xs,\n",
    "            self.y: labels\n",
    "        }\n",
    "\n",
    "        accuracy, loss = self.sess.run([self.accuracy, self.cross_entropy], feed_dict=feed_dict)\n",
    "        if (ii % 20 == 0):\n",
    "            time_str = datetime.datetime.now().isoformat()\n",
    "            print('#Testing# {}: Epoch {:>3} Batch {:>4}/{} accuracy = {:.3f}  test_loss = {:.5f}'.format(\n",
    "                    time_str,\n",
    "                    epoch_i,\n",
    "                    batch_i,\n",
    "                    batch_num,\n",
    "                    accuracy,\n",
    "                    loss))\n",
    "\n",
    "        return accuracy, loss\n",
    "            \n",
    "    def save(self):\n",
    "        save_path = self.saver.save(self.sess, os.path.join(self.save_dir, 'best_model.ckpt'))    \n",
    "        print(\"Model saved in file: {}\".format(save_path))\n",
    "        \n",
    "    def forward(self, xs):  \n",
    "        feed_dict = {\n",
    "            self.x: xs\n",
    "        }\n",
    "        logits = self.sess.run([tf.nn.softmax(self.logits)], feed_dict=feed_dict)\n",
    "        logits = np.reshape(np.array(logits), (n_classes))\n",
    "\n",
    "        return logits\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 开始训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conv1.shape =  (?, 28, 28, 100)\n",
      "conv2.shape =  (?, 12, 12, 150)\n",
      "max_pool conv2.shape =  (?, 6, 6, 150)\n",
      "conv3.shape =  (?, 6, 6, 250)\n",
      "max_pool conv3.shape =  (?, 3, 3, 250)\n",
      "fc0.shape =  (?, 2250)\n",
      "Could not find old network weights\n",
      "Training 2019-06-09T20:19:41.341654: Epoch   0 Batch    0/1875   train_loss = 2.99788\n",
      "Training 2019-06-09T20:20:00.219567: Epoch   0 Batch   20/1875   train_loss = 2.99611\n",
      "Training 2019-06-09T20:20:18.964997: Epoch   0 Batch   40/1875   train_loss = 2.99396\n",
      "Training 2019-06-09T20:20:38.782334: Epoch   0 Batch   60/1875   train_loss = 2.99717\n",
      "Training 2019-06-09T20:20:57.592695: Epoch   0 Batch   80/1875   train_loss = 2.98443\n",
      "Training 2019-06-09T20:21:16.464490: Epoch   0 Batch  100/1875   train_loss = 2.98561\n",
      "Training 2019-06-09T20:21:35.238281: Epoch   0 Batch  120/1875   train_loss = 2.97820\n",
      "Training 2019-06-09T20:21:54.033590: Epoch   0 Batch  140/1875   train_loss = 2.95885\n",
      "Training 2019-06-09T20:22:12.814279: Epoch   0 Batch  160/1875   train_loss = 2.89982\n",
      "Training 2019-06-09T20:22:31.729103: Epoch   0 Batch  180/1875   train_loss = 2.96169\n",
      "Training 2019-06-09T20:22:50.578145: Epoch   0 Batch  200/1875   train_loss = 2.89619\n",
      "Training 2019-06-09T20:23:09.391322: Epoch   0 Batch  220/1875   train_loss = 2.84215\n",
      "Training 2019-06-09T20:23:28.314114: Epoch   0 Batch  240/1875   train_loss = 2.92276\n",
      "Training 2019-06-09T20:23:47.196017: Epoch   0 Batch  260/1875   train_loss = 2.76000\n",
      "Training 2019-06-09T20:24:06.103758: Epoch   0 Batch  280/1875   train_loss = 2.87244\n",
      "Training 2019-06-09T20:24:24.904090: Epoch   0 Batch  300/1875   train_loss = 2.90142\n",
      "Training 2019-06-09T20:24:43.860518: Epoch   0 Batch  320/1875   train_loss = 2.77909\n",
      "Training 2019-06-09T20:25:02.784807: Epoch   0 Batch  340/1875   train_loss = 2.78389\n",
      "Training 2019-06-09T20:25:21.707398: Epoch   0 Batch  360/1875   train_loss = 2.86968\n",
      "Training 2019-06-09T20:25:40.625754: Epoch   0 Batch  380/1875   train_loss = 2.99491\n",
      "Training 2019-06-09T20:25:59.559029: Epoch   0 Batch  400/1875   train_loss = 2.78947\n",
      "Training 2019-06-09T20:26:18.466808: Epoch   0 Batch  420/1875   train_loss = 2.77603\n",
      "Training 2019-06-09T20:26:37.311627: Epoch   0 Batch  440/1875   train_loss = 2.71414\n",
      "Training 2019-06-09T20:26:56.252893: Epoch   0 Batch  460/1875   train_loss = 2.87222\n",
      "Training 2019-06-09T20:27:15.179273: Epoch   0 Batch  480/1875   train_loss = 2.82275\n",
      "Training 2019-06-09T20:27:34.089594: Epoch   0 Batch  500/1875   train_loss = 2.67935\n",
      "Training 2019-06-09T20:27:53.000410: Epoch   0 Batch  520/1875   train_loss = 2.65373\n",
      "Training 2019-06-09T20:28:12.819304: Epoch   0 Batch  540/1875   train_loss = 2.70658\n",
      "Training 2019-06-09T20:28:31.726263: Epoch   0 Batch  560/1875   train_loss = 2.66985\n",
      "Training 2019-06-09T20:28:50.616324: Epoch   0 Batch  580/1875   train_loss = 2.78240\n",
      "Training 2019-06-09T20:29:09.547775: Epoch   0 Batch  600/1875   train_loss = 2.71426\n",
      "Training 2019-06-09T20:29:28.588167: Epoch   0 Batch  620/1875   train_loss = 2.65379\n",
      "Training 2019-06-09T20:29:47.506953: Epoch   0 Batch  640/1875   train_loss = 2.75081\n",
      "Training 2019-06-09T20:30:06.428751: Epoch   0 Batch  660/1875   train_loss = 2.85143\n",
      "Training 2019-06-09T20:30:25.380379: Epoch   0 Batch  680/1875   train_loss = 2.77791\n",
      "Training 2019-06-09T20:30:44.347468: Epoch   0 Batch  700/1875   train_loss = 2.68978\n",
      "Training 2019-06-09T20:31:03.274441: Epoch   0 Batch  720/1875   train_loss = 2.57894\n",
      "Training 2019-06-09T20:31:22.310972: Epoch   0 Batch  740/1875   train_loss = 2.77347\n",
      "Training 2019-06-09T20:31:41.229070: Epoch   0 Batch  760/1875   train_loss = 2.63287\n",
      "Training 2019-06-09T20:32:00.184769: Epoch   0 Batch  780/1875   train_loss = 2.77555\n",
      "Training 2019-06-09T20:32:19.172356: Epoch   0 Batch  800/1875   train_loss = 2.67399\n",
      "Training 2019-06-09T20:32:38.173038: Epoch   0 Batch  820/1875   train_loss = 2.55420\n",
      "Training 2019-06-09T20:32:57.156391: Epoch   0 Batch  840/1875   train_loss = 2.63612\n",
      "Training 2019-06-09T20:33:17.106256: Epoch   0 Batch  860/1875   train_loss = 2.56234\n",
      "Training 2019-06-09T20:33:36.141753: Epoch   0 Batch  880/1875   train_loss = 2.72506\n",
      "Training 2019-06-09T20:33:55.719760: Epoch   0 Batch  900/1875   train_loss = 2.59526\n",
      "Training 2019-06-09T20:34:16.458387: Epoch   0 Batch  920/1875   train_loss = 2.71352\n",
      "Training 2019-06-09T20:34:35.418335: Epoch   0 Batch  940/1875   train_loss = 2.55131\n",
      "Training 2019-06-09T20:34:54.380109: Epoch   0 Batch  960/1875   train_loss = 2.44441\n",
      "Training 2019-06-09T20:35:13.381455: Epoch   0 Batch  980/1875   train_loss = 2.68384\n",
      "Training 2019-06-09T20:35:35.120753: Epoch   0 Batch 1000/1875   train_loss = 2.60861\n",
      "Training 2019-06-09T20:35:54.624025: Epoch   0 Batch 1020/1875   train_loss = 2.66213\n",
      "Training 2019-06-09T20:36:17.362163: Epoch   0 Batch 1040/1875   train_loss = 2.67060\n",
      "Training 2019-06-09T20:36:40.771623: Epoch   0 Batch 1060/1875   train_loss = 2.51431\n",
      "Training 2019-06-09T20:37:00.155575: Epoch   0 Batch 1080/1875   train_loss = 2.43607\n",
      "Training 2019-06-09T20:37:19.190138: Epoch   0 Batch 1100/1875   train_loss = 2.58182\n",
      "Training 2019-06-09T20:37:38.168484: Epoch   0 Batch 1120/1875   train_loss = 2.61113\n",
      "Training 2019-06-09T20:38:00.233964: Epoch   0 Batch 1140/1875   train_loss = 2.69965\n",
      "Training 2019-06-09T20:38:21.681428: Epoch   0 Batch 1160/1875   train_loss = 2.49744\n",
      "Training 2019-06-09T20:38:45.381178: Epoch   0 Batch 1180/1875   train_loss = 2.57460\n",
      "Training 2019-06-09T20:39:07.540363: Epoch   0 Batch 1200/1875   train_loss = 2.43121\n",
      "Training 2019-06-09T20:39:28.420673: Epoch   0 Batch 1220/1875   train_loss = 2.64205\n",
      "Training 2019-06-09T20:39:48.634692: Epoch   0 Batch 1240/1875   train_loss = 2.38422\n",
      "Training 2019-06-09T20:40:09.842397: Epoch   0 Batch 1260/1875   train_loss = 2.57659\n",
      "Training 2019-06-09T20:40:31.428775: Epoch   0 Batch 1280/1875   train_loss = 2.54219\n",
      "Training 2019-06-09T20:40:50.461527: Epoch   0 Batch 1300/1875   train_loss = 2.41447\n",
      "Training 2019-06-09T20:41:09.479303: Epoch   0 Batch 1320/1875   train_loss = 2.69706\n",
      "Training 2019-06-09T20:41:28.607863: Epoch   0 Batch 1340/1875   train_loss = 2.42009\n",
      "Training 2019-06-09T20:41:47.514087: Epoch   0 Batch 1360/1875   train_loss = 2.49435\n",
      "Training 2019-06-09T20:42:06.500040: Epoch   0 Batch 1380/1875   train_loss = 2.60897\n",
      "Training 2019-06-09T20:42:25.642616: Epoch   0 Batch 1400/1875   train_loss = 2.64388\n",
      "Training 2019-06-09T20:42:44.574832: Epoch   0 Batch 1420/1875   train_loss = 2.42363\n",
      "Training 2019-06-09T20:43:03.616657: Epoch   0 Batch 1440/1875   train_loss = 2.42265\n",
      "Training 2019-06-09T20:43:22.654385: Epoch   0 Batch 1460/1875   train_loss = 2.43193\n",
      "Training 2019-06-09T20:43:41.643883: Epoch   0 Batch 1480/1875   train_loss = 2.35349\n",
      "Training 2019-06-09T20:44:00.654324: Epoch   0 Batch 1500/1875   train_loss = 2.43567\n",
      "Training 2019-06-09T20:44:19.660893: Epoch   0 Batch 1520/1875   train_loss = 2.40709\n",
      "Training 2019-06-09T20:44:38.636167: Epoch   0 Batch 1540/1875   train_loss = 2.51836\n",
      "Training 2019-06-09T20:44:57.644783: Epoch   0 Batch 1560/1875   train_loss = 2.51043\n",
      "Training 2019-06-09T20:45:16.665734: Epoch   0 Batch 1580/1875   train_loss = 2.59373\n",
      "Training 2019-06-09T20:45:35.716978: Epoch   0 Batch 1600/1875   train_loss = 2.48191\n",
      "Training 2019-06-09T20:45:56.177064: Epoch   0 Batch 1620/1875   train_loss = 2.52361\n",
      "Training 2019-06-09T20:46:15.175040: Epoch   0 Batch 1640/1875   train_loss = 2.37528\n",
      "Training 2019-06-09T20:46:34.225606: Epoch   0 Batch 1660/1875   train_loss = 2.47560\n",
      "Training 2019-06-09T20:46:53.214042: Epoch   0 Batch 1680/1875   train_loss = 2.51842\n",
      "Training 2019-06-09T20:47:12.205898: Epoch   0 Batch 1700/1875   train_loss = 2.41781\n",
      "Training 2019-06-09T20:47:31.340847: Epoch   0 Batch 1720/1875   train_loss = 2.46864\n",
      "Training 2019-06-09T20:47:50.297728: Epoch   0 Batch 1740/1875   train_loss = 2.27639\n",
      "Training 2019-06-09T20:48:09.325165: Epoch   0 Batch 1760/1875   train_loss = 2.60109\n",
      "Training 2019-06-09T20:48:28.374103: Epoch   0 Batch 1780/1875   train_loss = 2.63439\n",
      "Training 2019-06-09T20:48:47.356683: Epoch   0 Batch 1800/1875   train_loss = 2.50116\n",
      "Training 2019-06-09T20:49:06.318056: Epoch   0 Batch 1820/1875   train_loss = 2.37711\n",
      "Training 2019-06-09T20:49:25.427721: Epoch   0 Batch 1840/1875   train_loss = 2.55440\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training 2019-06-09T20:49:44.470360: Epoch   0 Batch 1860/1875   train_loss = 2.48097\n",
      "#Testing# 2019-06-09T20:49:58.315169: Epoch   0 Batch    0/468 accuracy = 0.344  test_loss = 2.25901\n",
      "#Testing# 2019-06-09T20:50:04.986812: Epoch   0 Batch   20/468 accuracy = 0.234  test_loss = 2.36429\n",
      "#Testing# 2019-06-09T20:50:11.666846: Epoch   0 Batch   40/468 accuracy = 0.219  test_loss = 2.50277\n",
      "#Testing# 2019-06-09T20:50:18.375389: Epoch   0 Batch   60/468 accuracy = 0.203  test_loss = 2.49642\n",
      "#Testing# 2019-06-09T20:50:25.058985: Epoch   0 Batch   80/468 accuracy = 0.203  test_loss = 2.36249\n",
      "#Testing# 2019-06-09T20:50:31.740721: Epoch   0 Batch  100/468 accuracy = 0.281  test_loss = 2.38989\n",
      "#Testing# 2019-06-09T20:50:38.426410: Epoch   0 Batch  120/468 accuracy = 0.234  test_loss = 2.48016\n",
      "#Testing# 2019-06-09T20:50:45.103645: Epoch   0 Batch  140/468 accuracy = 0.281  test_loss = 2.36974\n",
      "#Testing# 2019-06-09T20:50:52.333436: Epoch   0 Batch  160/468 accuracy = 0.219  test_loss = 2.48809\n",
      "#Testing# 2019-06-09T20:50:59.018587: Epoch   0 Batch  180/468 accuracy = 0.250  test_loss = 2.33702\n",
      "#Testing# 2019-06-09T20:51:05.684114: Epoch   0 Batch  200/468 accuracy = 0.266  test_loss = 2.27976\n",
      "#Testing# 2019-06-09T20:51:12.357402: Epoch   0 Batch  220/468 accuracy = 0.281  test_loss = 2.38512\n",
      "#Testing# 2019-06-09T20:51:19.079202: Epoch   0 Batch  240/468 accuracy = 0.219  test_loss = 2.57667\n",
      "#Testing# 2019-06-09T20:51:25.873527: Epoch   0 Batch  260/468 accuracy = 0.375  test_loss = 2.17272\n",
      "#Testing# 2019-06-09T20:51:34.877008: Epoch   0 Batch  280/468 accuracy = 0.328  test_loss = 2.31632\n",
      "#Testing# 2019-06-09T20:51:42.069456: Epoch   0 Batch  300/468 accuracy = 0.219  test_loss = 2.41082\n",
      "#Testing# 2019-06-09T20:51:50.639670: Epoch   0 Batch  320/468 accuracy = 0.297  test_loss = 2.21281\n",
      "#Testing# 2019-06-09T20:51:59.268830: Epoch   0 Batch  340/468 accuracy = 0.188  test_loss = 2.52272\n",
      "#Testing# 2019-06-09T20:52:07.328644: Epoch   0 Batch  360/468 accuracy = 0.266  test_loss = 2.31120\n",
      "#Testing# 2019-06-09T20:52:15.239291: Epoch   0 Batch  380/468 accuracy = 0.312  test_loss = 2.23697\n",
      "#Testing# 2019-06-09T20:52:23.154748: Epoch   0 Batch  400/468 accuracy = 0.297  test_loss = 2.29510\n",
      "#Testing# 2019-06-09T20:52:30.815280: Epoch   0 Batch  420/468 accuracy = 0.297  test_loss = 2.36257\n",
      "#Testing# 2019-06-09T20:52:38.303130: Epoch   0 Batch  440/468 accuracy = 0.344  test_loss = 2.21877\n",
      "#Testing# 2019-06-09T20:52:45.439851: Epoch   0 Batch  460/468 accuracy = 0.172  test_loss = 2.62057\n",
      "best loss = 2.4291252661974  acc = 0.25747863247863245\n",
      "Model saved in file: ./session/best_model.ckpt\n",
      "Training 2019-06-09T20:52:54.763246: Epoch   1 Batch    5/1875   train_loss = 2.40329\n",
      "Training 2019-06-09T20:53:16.216566: Epoch   1 Batch   25/1875   train_loss = 2.38993\n",
      "Training 2019-06-09T20:53:36.721818: Epoch   1 Batch   45/1875   train_loss = 2.39356\n",
      "Training 2019-06-09T20:53:59.125463: Epoch   1 Batch   65/1875   train_loss = 2.64404\n",
      "Training 2019-06-09T20:54:21.891636: Epoch   1 Batch   85/1875   train_loss = 2.31207\n",
      "Training 2019-06-09T20:54:41.632337: Epoch   1 Batch  105/1875   train_loss = 2.42547\n",
      "Training 2019-06-09T20:55:01.372215: Epoch   1 Batch  125/1875   train_loss = 2.44548\n",
      "Training 2019-06-09T20:55:22.789968: Epoch   1 Batch  145/1875   train_loss = 2.48667\n",
      "Training 2019-06-09T20:55:42.541518: Epoch   1 Batch  165/1875   train_loss = 2.43101\n",
      "Training 2019-06-09T20:56:02.183102: Epoch   1 Batch  185/1875   train_loss = 2.25625\n",
      "Training 2019-06-09T20:56:26.689021: Epoch   1 Batch  205/1875   train_loss = 2.44835\n",
      "Training 2019-06-09T20:56:47.922481: Epoch   1 Batch  225/1875   train_loss = 2.28889\n",
      "Training 2019-06-09T20:57:09.874284: Epoch   1 Batch  245/1875   train_loss = 2.34449\n",
      "Training 2019-06-09T20:57:31.813592: Epoch   1 Batch  265/1875   train_loss = 2.41355\n",
      "Training 2019-06-09T20:57:51.847733: Epoch   1 Batch  285/1875   train_loss = 2.29643\n",
      "Training 2019-06-09T20:58:10.864448: Epoch   1 Batch  305/1875   train_loss = 2.36909\n",
      "Training 2019-06-09T20:58:30.215860: Epoch   1 Batch  325/1875   train_loss = 2.41912\n",
      "Training 2019-06-09T20:58:52.822464: Epoch   1 Batch  345/1875   train_loss = 2.70256\n",
      "Training 2019-06-09T20:59:15.816313: Epoch   1 Batch  365/1875   train_loss = 2.52658\n",
      "Training 2019-06-09T20:59:37.057172: Epoch   1 Batch  385/1875   train_loss = 2.32470\n",
      "Training 2019-06-09T20:59:56.998323: Epoch   1 Batch  405/1875   train_loss = 2.33352\n",
      "Training 2019-06-09T21:00:17.434151: Epoch   1 Batch  425/1875   train_loss = 2.39848\n",
      "Training 2019-06-09T21:00:36.886987: Epoch   1 Batch  445/1875   train_loss = 2.55065\n",
      "Training 2019-06-09T21:00:58.108549: Epoch   1 Batch  465/1875   train_loss = 2.05868\n",
      "Training 2019-06-09T21:01:20.351040: Epoch   1 Batch  485/1875   train_loss = 2.36084\n",
      "Training 2019-06-09T21:01:40.267683: Epoch   1 Batch  505/1875   train_loss = 2.26505\n",
      "Training 2019-06-09T21:02:02.004874: Epoch   1 Batch  525/1875   train_loss = 2.27791\n",
      "Training 2019-06-09T21:02:24.718074: Epoch   1 Batch  545/1875   train_loss = 2.42896\n",
      "Training 2019-06-09T21:02:46.150290: Epoch   1 Batch  565/1875   train_loss = 2.25380\n",
      "Training 2019-06-09T21:03:09.244586: Epoch   1 Batch  585/1875   train_loss = 2.38327\n",
      "Training 2019-06-09T21:03:28.841327: Epoch   1 Batch  605/1875   train_loss = 2.56020\n",
      "Training 2019-06-09T21:03:48.050579: Epoch   1 Batch  625/1875   train_loss = 2.22433\n",
      "Training 2019-06-09T21:04:07.126085: Epoch   1 Batch  645/1875   train_loss = 2.10775\n",
      "Training 2019-06-09T21:04:26.093870: Epoch   1 Batch  665/1875   train_loss = 2.28529\n",
      "Training 2019-06-09T21:04:45.127166: Epoch   1 Batch  685/1875   train_loss = 2.25479\n",
      "Training 2019-06-09T21:05:04.203457: Epoch   1 Batch  705/1875   train_loss = 2.54223\n",
      "Training 2019-06-09T21:05:23.258061: Epoch   1 Batch  725/1875   train_loss = 2.36769\n",
      "Training 2019-06-09T21:05:42.313623: Epoch   1 Batch  745/1875   train_loss = 2.03327\n",
      "Training 2019-06-09T21:06:01.284218: Epoch   1 Batch  765/1875   train_loss = 2.54647\n",
      "Training 2019-06-09T21:06:20.333298: Epoch   1 Batch  785/1875   train_loss = 2.28503\n",
      "Training 2019-06-09T21:06:39.433713: Epoch   1 Batch  805/1875   train_loss = 2.34066\n",
      "Training 2019-06-09T21:06:58.463828: Epoch   1 Batch  825/1875   train_loss = 2.22609\n",
      "Training 2019-06-09T21:07:17.456506: Epoch   1 Batch  845/1875   train_loss = 2.45788\n",
      "Training 2019-06-09T21:07:36.518000: Epoch   1 Batch  865/1875   train_loss = 2.20459\n",
      "Training 2019-06-09T21:07:55.540497: Epoch   1 Batch  885/1875   train_loss = 2.26502\n",
      "Training 2019-06-09T21:08:14.571669: Epoch   1 Batch  905/1875   train_loss = 2.44630\n",
      "Training 2019-06-09T21:08:33.527291: Epoch   1 Batch  925/1875   train_loss = 2.15357\n",
      "Training 2019-06-09T21:08:53.395785: Epoch   1 Batch  945/1875   train_loss = 2.48353\n",
      "Training 2019-06-09T21:09:12.925780: Epoch   1 Batch  965/1875   train_loss = 2.21708\n",
      "Training 2019-06-09T21:09:32.052111: Epoch   1 Batch  985/1875   train_loss = 2.29100\n",
      "Training 2019-06-09T21:09:51.058110: Epoch   1 Batch 1005/1875   train_loss = 2.12697\n",
      "Training 2019-06-09T21:10:10.129135: Epoch   1 Batch 1025/1875   train_loss = 2.11427\n",
      "Training 2019-06-09T21:10:29.224705: Epoch   1 Batch 1045/1875   train_loss = 2.26301\n",
      "Training 2019-06-09T21:10:48.227497: Epoch   1 Batch 1065/1875   train_loss = 2.16185\n",
      "Training 2019-06-09T21:11:07.298670: Epoch   1 Batch 1085/1875   train_loss = 2.23464\n",
      "Training 2019-06-09T21:11:26.404128: Epoch   1 Batch 1105/1875   train_loss = 2.05799\n",
      "Training 2019-06-09T21:11:45.448106: Epoch   1 Batch 1125/1875   train_loss = 2.41158\n",
      "Training 2019-06-09T21:12:04.457685: Epoch   1 Batch 1145/1875   train_loss = 2.30566\n",
      "Training 2019-06-09T21:12:23.576306: Epoch   1 Batch 1165/1875   train_loss = 2.19628\n",
      "Training 2019-06-09T21:12:43.193900: Epoch   1 Batch 1185/1875   train_loss = 2.43265\n",
      "Training 2019-06-09T21:13:02.297711: Epoch   1 Batch 1205/1875   train_loss = 2.27384\n",
      "Training 2019-06-09T21:13:21.444258: Epoch   1 Batch 1225/1875   train_loss = 2.24340\n",
      "Training 2019-06-09T21:13:40.495890: Epoch   1 Batch 1245/1875   train_loss = 2.28298\n",
      "Training 2019-06-09T21:13:59.618137: Epoch   1 Batch 1265/1875   train_loss = 2.37488\n",
      "Training 2019-06-09T21:14:18.694391: Epoch   1 Batch 1285/1875   train_loss = 2.52194\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training 2019-06-09T21:14:37.722854: Epoch   1 Batch 1305/1875   train_loss = 2.12835\n",
      "Training 2019-06-09T21:14:56.820860: Epoch   1 Batch 1325/1875   train_loss = 2.18542\n",
      "Training 2019-06-09T21:15:15.841435: Epoch   1 Batch 1345/1875   train_loss = 2.20790\n",
      "Training 2019-06-09T21:15:34.893095: Epoch   1 Batch 1365/1875   train_loss = 2.45721\n",
      "Training 2019-06-09T21:15:53.966006: Epoch   1 Batch 1385/1875   train_loss = 1.97172\n",
      "Training 2019-06-09T21:16:13.005496: Epoch   1 Batch 1405/1875   train_loss = 2.44807\n",
      "Training 2019-06-09T21:16:33.528596: Epoch   1 Batch 1425/1875   train_loss = 2.17413\n",
      "Training 2019-06-09T21:16:52.615904: Epoch   1 Batch 1445/1875   train_loss = 2.35742\n",
      "Training 2019-06-09T21:17:11.795621: Epoch   1 Batch 1465/1875   train_loss = 2.23739\n",
      "Training 2019-06-09T21:17:30.932954: Epoch   1 Batch 1485/1875   train_loss = 2.07905\n",
      "Training 2019-06-09T21:17:49.965148: Epoch   1 Batch 1505/1875   train_loss = 2.48690\n",
      "Training 2019-06-09T21:18:09.023241: Epoch   1 Batch 1525/1875   train_loss = 2.19645\n",
      "Training 2019-06-09T21:18:28.096932: Epoch   1 Batch 1545/1875   train_loss = 2.38119\n",
      "Training 2019-06-09T21:18:47.088052: Epoch   1 Batch 1565/1875   train_loss = 2.32537\n",
      "Training 2019-06-09T21:19:06.125637: Epoch   1 Batch 1585/1875   train_loss = 2.40239\n",
      "Training 2019-06-09T21:19:25.242746: Epoch   1 Batch 1605/1875   train_loss = 2.26622\n",
      "Training 2019-06-09T21:19:44.297742: Epoch   1 Batch 1625/1875   train_loss = 2.21955\n",
      "Training 2019-06-09T21:20:03.328275: Epoch   1 Batch 1645/1875   train_loss = 2.06832\n",
      "Training 2019-06-09T21:20:22.398735: Epoch   1 Batch 1665/1875   train_loss = 2.38333\n",
      "Training 2019-06-09T21:20:41.384809: Epoch   1 Batch 1685/1875   train_loss = 2.03331\n",
      "Training 2019-06-09T21:21:00.403252: Epoch   1 Batch 1705/1875   train_loss = 2.07354\n",
      "Training 2019-06-09T21:21:19.389327: Epoch   1 Batch 1725/1875   train_loss = 2.07450\n",
      "Training 2019-06-09T21:21:38.478758: Epoch   1 Batch 1745/1875   train_loss = 2.05758\n",
      "Training 2019-06-09T21:21:57.478884: Epoch   1 Batch 1765/1875   train_loss = 2.10964\n",
      "Training 2019-06-09T21:22:16.529256: Epoch   1 Batch 1785/1875   train_loss = 2.45421\n",
      "Training 2019-06-09T21:22:35.582141: Epoch   1 Batch 1805/1875   train_loss = 2.15960\n",
      "Training 2019-06-09T21:22:55.366152: Epoch   1 Batch 1825/1875   train_loss = 1.97798\n",
      "Training 2019-06-09T21:23:15.179506: Epoch   1 Batch 1845/1875   train_loss = 2.25741\n",
      "Training 2019-06-09T21:23:34.323709: Epoch   1 Batch 1865/1875   train_loss = 2.21134\n",
      "#Testing# 2019-06-09T21:23:47.194681: Epoch   1 Batch   12/468 accuracy = 0.406  test_loss = 1.99430\n",
      "#Testing# 2019-06-09T21:23:53.875465: Epoch   1 Batch   32/468 accuracy = 0.391  test_loss = 2.07075\n",
      "#Testing# 2019-06-09T21:24:00.536793: Epoch   1 Batch   52/468 accuracy = 0.359  test_loss = 2.25715\n",
      "#Testing# 2019-06-09T21:24:07.209589: Epoch   1 Batch   72/468 accuracy = 0.328  test_loss = 2.45505\n",
      "#Testing# 2019-06-09T21:24:13.897508: Epoch   1 Batch   92/468 accuracy = 0.344  test_loss = 1.94656\n",
      "#Testing# 2019-06-09T21:24:20.554890: Epoch   1 Batch  112/468 accuracy = 0.406  test_loss = 2.11133\n",
      "#Testing# 2019-06-09T21:24:27.219575: Epoch   1 Batch  132/468 accuracy = 0.359  test_loss = 2.04643\n",
      "#Testing# 2019-06-09T21:24:33.881408: Epoch   1 Batch  152/468 accuracy = 0.297  test_loss = 1.99954\n",
      "#Testing# 2019-06-09T21:24:40.534641: Epoch   1 Batch  172/468 accuracy = 0.328  test_loss = 2.05382\n",
      "#Testing# 2019-06-09T21:24:47.212899: Epoch   1 Batch  192/468 accuracy = 0.328  test_loss = 2.17120\n",
      "#Testing# 2019-06-09T21:24:53.892369: Epoch   1 Batch  212/468 accuracy = 0.391  test_loss = 2.13409\n",
      "#Testing# 2019-06-09T21:25:00.560388: Epoch   1 Batch  232/468 accuracy = 0.344  test_loss = 2.08719\n",
      "#Testing# 2019-06-09T21:25:07.231866: Epoch   1 Batch  252/468 accuracy = 0.406  test_loss = 1.98768\n",
      "#Testing# 2019-06-09T21:25:13.909633: Epoch   1 Batch  272/468 accuracy = 0.391  test_loss = 2.08233\n",
      "#Testing# 2019-06-09T21:25:20.587447: Epoch   1 Batch  292/468 accuracy = 0.406  test_loss = 2.00548\n",
      "#Testing# 2019-06-09T21:25:27.267091: Epoch   1 Batch  312/468 accuracy = 0.328  test_loss = 2.40829\n",
      "#Testing# 2019-06-09T21:25:33.939004: Epoch   1 Batch  332/468 accuracy = 0.344  test_loss = 2.18266\n",
      "#Testing# 2019-06-09T21:25:40.608037: Epoch   1 Batch  352/468 accuracy = 0.359  test_loss = 2.01551\n",
      "#Testing# 2019-06-09T21:25:47.442550: Epoch   1 Batch  372/468 accuracy = 0.344  test_loss = 2.18161\n",
      "#Testing# 2019-06-09T21:25:54.124792: Epoch   1 Batch  392/468 accuracy = 0.328  test_loss = 2.15943\n",
      "#Testing# 2019-06-09T21:26:00.837452: Epoch   1 Batch  412/468 accuracy = 0.359  test_loss = 2.12441\n",
      "#Testing# 2019-06-09T21:26:07.521996: Epoch   1 Batch  432/468 accuracy = 0.422  test_loss = 2.17349\n",
      "#Testing# 2019-06-09T21:26:14.210592: Epoch   1 Batch  452/468 accuracy = 0.328  test_loss = 2.29489\n",
      "best loss = 2.1918396763821955  acc = 0.32779113247863245\n",
      "Model saved in file: ./session/best_model.ckpt\n",
      "Training 2019-06-09T21:26:30.034288: Epoch   2 Batch   10/1875   train_loss = 1.84271\n",
      "Training 2019-06-09T21:26:48.921928: Epoch   2 Batch   30/1875   train_loss = 2.22468\n",
      "Training 2019-06-09T21:27:07.830701: Epoch   2 Batch   50/1875   train_loss = 2.09566\n",
      "Training 2019-06-09T21:27:26.748597: Epoch   2 Batch   70/1875   train_loss = 2.11921\n",
      "Training 2019-06-09T21:27:45.634870: Epoch   2 Batch   90/1875   train_loss = 2.20856\n",
      "Training 2019-06-09T21:28:04.546477: Epoch   2 Batch  110/1875   train_loss = 2.22707\n",
      "Training 2019-06-09T21:28:27.194989: Epoch   2 Batch  130/1875   train_loss = 2.11016\n",
      "Training 2019-06-09T21:28:48.308614: Epoch   2 Batch  150/1875   train_loss = 2.12078\n",
      "Training 2019-06-09T21:29:07.537008: Epoch   2 Batch  170/1875   train_loss = 2.40670\n",
      "Training 2019-06-09T21:29:26.661663: Epoch   2 Batch  190/1875   train_loss = 2.40389\n",
      "Training 2019-06-09T21:29:45.672244: Epoch   2 Batch  210/1875   train_loss = 2.25630\n",
      "Training 2019-06-09T21:30:04.703684: Epoch   2 Batch  230/1875   train_loss = 2.01547\n",
      "Training 2019-06-09T21:30:23.795847: Epoch   2 Batch  250/1875   train_loss = 2.46506\n",
      "Training 2019-06-09T21:30:42.819563: Epoch   2 Batch  270/1875   train_loss = 1.82503\n",
      "Training 2019-06-09T21:31:01.911063: Epoch   2 Batch  290/1875   train_loss = 1.88726\n",
      "Training 2019-06-09T21:31:20.908916: Epoch   2 Batch  310/1875   train_loss = 2.31687\n",
      "Training 2019-06-09T21:31:39.936979: Epoch   2 Batch  330/1875   train_loss = 1.87889\n",
      "Training 2019-06-09T21:31:59.002292: Epoch   2 Batch  350/1875   train_loss = 2.04391\n",
      "Training 2019-06-09T21:32:18.076237: Epoch   2 Batch  370/1875   train_loss = 2.06428\n",
      "Training 2019-06-09T21:32:37.150056: Epoch   2 Batch  390/1875   train_loss = 2.07374\n",
      "Training 2019-06-09T21:32:56.288345: Epoch   2 Batch  410/1875   train_loss = 2.24135\n",
      "Training 2019-06-09T21:33:15.331031: Epoch   2 Batch  430/1875   train_loss = 2.22732\n",
      "Training 2019-06-09T21:33:34.625610: Epoch   2 Batch  450/1875   train_loss = 1.88424\n",
      "Training 2019-06-09T21:33:53.647673: Epoch   2 Batch  470/1875   train_loss = 2.37663\n",
      "Training 2019-06-09T21:34:12.686118: Epoch   2 Batch  490/1875   train_loss = 2.01098\n",
      "Training 2019-06-09T21:34:31.838289: Epoch   2 Batch  510/1875   train_loss = 1.87004\n",
      "Training 2019-06-09T21:34:50.832031: Epoch   2 Batch  530/1875   train_loss = 2.19404\n",
      "Training 2019-06-09T21:35:09.781873: Epoch   2 Batch  550/1875   train_loss = 1.87297\n",
      "Training 2019-06-09T21:35:28.797237: Epoch   2 Batch  570/1875   train_loss = 2.12421\n",
      "Training 2019-06-09T21:35:47.801500: Epoch   2 Batch  590/1875   train_loss = 2.18014\n",
      "Training 2019-06-09T21:36:06.807772: Epoch   2 Batch  610/1875   train_loss = 2.25240\n",
      "Training 2019-06-09T21:36:25.881086: Epoch   2 Batch  630/1875   train_loss = 2.00221\n",
      "Training 2019-06-09T21:36:44.931692: Epoch   2 Batch  650/1875   train_loss = 1.99941\n",
      "Training 2019-06-09T21:37:04.007854: Epoch   2 Batch  670/1875   train_loss = 2.10761\n",
      "Training 2019-06-09T21:37:23.021589: Epoch   2 Batch  690/1875   train_loss = 1.98927\n",
      "Training 2019-06-09T21:37:42.068301: Epoch   2 Batch  710/1875   train_loss = 2.07184\n",
      "Training 2019-06-09T21:38:01.279100: Epoch   2 Batch  730/1875   train_loss = 1.96539\n",
      "Training 2019-06-09T21:38:20.768017: Epoch   2 Batch  750/1875   train_loss = 1.88133\n",
      "Training 2019-06-09T21:38:40.294443: Epoch   2 Batch  770/1875   train_loss = 2.02282\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training 2019-06-09T21:38:59.603547: Epoch   2 Batch  790/1875   train_loss = 2.20324\n",
      "Training 2019-06-09T21:39:18.486534: Epoch   2 Batch  810/1875   train_loss = 1.94314\n",
      "Training 2019-06-09T21:39:37.400845: Epoch   2 Batch  830/1875   train_loss = 2.48222\n",
      "Training 2019-06-09T21:39:56.329811: Epoch   2 Batch  850/1875   train_loss = 2.13817\n",
      "Training 2019-06-09T21:40:15.276380: Epoch   2 Batch  870/1875   train_loss = 2.09990\n",
      "Training 2019-06-09T21:40:34.157197: Epoch   2 Batch  890/1875   train_loss = 2.02358\n",
      "Training 2019-06-09T21:40:53.032413: Epoch   2 Batch  910/1875   train_loss = 1.96964\n",
      "Training 2019-06-09T21:41:11.981852: Epoch   2 Batch  930/1875   train_loss = 2.13029\n",
      "Training 2019-06-09T21:41:30.906653: Epoch   2 Batch  950/1875   train_loss = 2.18091\n",
      "Training 2019-06-09T21:41:49.760378: Epoch   2 Batch  970/1875   train_loss = 2.04798\n",
      "Training 2019-06-09T21:42:08.616007: Epoch   2 Batch  990/1875   train_loss = 2.00484\n",
      "Training 2019-06-09T21:42:27.508620: Epoch   2 Batch 1010/1875   train_loss = 2.09828\n",
      "Training 2019-06-09T21:42:46.350311: Epoch   2 Batch 1030/1875   train_loss = 2.23003\n",
      "Training 2019-06-09T21:43:05.385001: Epoch   2 Batch 1050/1875   train_loss = 2.15776\n",
      "Training 2019-06-09T21:43:24.241115: Epoch   2 Batch 1070/1875   train_loss = 1.93957\n",
      "Training 2019-06-09T21:43:43.058086: Epoch   2 Batch 1090/1875   train_loss = 2.29826\n",
      "Training 2019-06-09T21:44:01.913669: Epoch   2 Batch 1110/1875   train_loss = 2.02599\n",
      "Training 2019-06-09T21:44:20.805384: Epoch   2 Batch 1130/1875   train_loss = 2.01175\n",
      "Training 2019-06-09T21:44:39.620297: Epoch   2 Batch 1150/1875   train_loss = 1.97363\n",
      "Training 2019-06-09T21:44:58.511076: Epoch   2 Batch 1170/1875   train_loss = 2.05625\n",
      "Training 2019-06-09T21:45:17.393287: Epoch   2 Batch 1190/1875   train_loss = 1.84029\n",
      "Training 2019-06-09T21:45:36.294626: Epoch   2 Batch 1210/1875   train_loss = 2.35220\n",
      "Training 2019-06-09T21:45:55.185823: Epoch   2 Batch 1230/1875   train_loss = 2.10819\n",
      "Training 2019-06-09T21:46:14.091924: Epoch   2 Batch 1250/1875   train_loss = 1.87437\n",
      "Training 2019-06-09T21:46:32.957405: Epoch   2 Batch 1270/1875   train_loss = 2.01616\n",
      "Training 2019-06-09T21:46:51.792475: Epoch   2 Batch 1290/1875   train_loss = 1.77897\n",
      "Training 2019-06-09T21:47:10.685719: Epoch   2 Batch 1310/1875   train_loss = 2.06685\n",
      "Training 2019-06-09T21:47:29.624358: Epoch   2 Batch 1330/1875   train_loss = 2.14175\n",
      "Training 2019-06-09T21:47:48.587199: Epoch   2 Batch 1350/1875   train_loss = 2.19684\n",
      "Training 2019-06-09T21:48:07.463862: Epoch   2 Batch 1370/1875   train_loss = 1.83868\n",
      "Training 2019-06-09T21:48:26.417097: Epoch   2 Batch 1390/1875   train_loss = 2.22057\n",
      "Training 2019-06-09T21:48:45.245261: Epoch   2 Batch 1410/1875   train_loss = 2.10871\n",
      "Training 2019-06-09T21:49:04.113447: Epoch   2 Batch 1430/1875   train_loss = 2.14917\n",
      "Training 2019-06-09T21:49:22.982245: Epoch   2 Batch 1450/1875   train_loss = 2.28995\n",
      "Training 2019-06-09T21:49:43.502016: Epoch   2 Batch 1470/1875   train_loss = 2.01890\n",
      "Training 2019-06-09T21:50:02.471515: Epoch   2 Batch 1490/1875   train_loss = 1.94093\n",
      "Training 2019-06-09T21:50:21.310951: Epoch   2 Batch 1510/1875   train_loss = 2.04827\n",
      "Training 2019-06-09T21:50:40.165656: Epoch   2 Batch 1530/1875   train_loss = 2.01632\n",
      "Training 2019-06-09T21:50:59.015036: Epoch   2 Batch 1550/1875   train_loss = 2.03613\n",
      "Training 2019-06-09T21:51:17.953164: Epoch   2 Batch 1570/1875   train_loss = 1.83628\n",
      "Training 2019-06-09T21:51:36.849058: Epoch   2 Batch 1590/1875   train_loss = 2.08994\n",
      "Training 2019-06-09T21:51:55.735119: Epoch   2 Batch 1610/1875   train_loss = 2.02045\n",
      "Training 2019-06-09T21:52:14.661549: Epoch   2 Batch 1630/1875   train_loss = 1.93773\n",
      "Training 2019-06-09T21:52:33.573873: Epoch   2 Batch 1650/1875   train_loss = 1.99619\n",
      "Training 2019-06-09T21:52:52.484809: Epoch   2 Batch 1670/1875   train_loss = 1.89643\n",
      "Training 2019-06-09T21:53:11.393483: Epoch   2 Batch 1690/1875   train_loss = 1.98936\n",
      "Training 2019-06-09T21:53:30.255975: Epoch   2 Batch 1710/1875   train_loss = 2.17133\n",
      "Training 2019-06-09T21:53:49.180042: Epoch   2 Batch 1730/1875   train_loss = 1.91787\n",
      "Training 2019-06-09T21:54:08.036913: Epoch   2 Batch 1750/1875   train_loss = 2.22487\n",
      "Training 2019-06-09T21:54:26.923441: Epoch   2 Batch 1770/1875   train_loss = 2.15466\n",
      "Training 2019-06-09T21:54:45.772326: Epoch   2 Batch 1790/1875   train_loss = 2.13657\n",
      "Training 2019-06-09T21:55:04.704488: Epoch   2 Batch 1810/1875   train_loss = 1.92347\n",
      "Training 2019-06-09T21:55:23.555924: Epoch   2 Batch 1830/1875   train_loss = 2.15408\n",
      "Training 2019-06-09T21:55:42.431481: Epoch   2 Batch 1850/1875   train_loss = 1.78711\n",
      "Training 2019-06-09T21:56:01.305840: Epoch   2 Batch 1870/1875   train_loss = 1.89922\n",
      "#Testing# 2019-06-09T21:56:06.734373: Epoch   2 Batch    4/468 accuracy = 0.297  test_loss = 2.26892\n",
      "#Testing# 2019-06-09T21:56:13.407538: Epoch   2 Batch   24/468 accuracy = 0.375  test_loss = 2.03943\n",
      "#Testing# 2019-06-09T21:56:20.076815: Epoch   2 Batch   44/468 accuracy = 0.422  test_loss = 2.14653\n",
      "#Testing# 2019-06-09T21:56:26.729436: Epoch   2 Batch   64/468 accuracy = 0.328  test_loss = 2.10221\n",
      "#Testing# 2019-06-09T21:56:33.394474: Epoch   2 Batch   84/468 accuracy = 0.281  test_loss = 2.19712\n",
      "#Testing# 2019-06-09T21:56:40.038021: Epoch   2 Batch  104/468 accuracy = 0.438  test_loss = 1.98135\n",
      "#Testing# 2019-06-09T21:56:46.690951: Epoch   2 Batch  124/468 accuracy = 0.406  test_loss = 1.94859\n",
      "#Testing# 2019-06-09T21:56:53.351196: Epoch   2 Batch  144/468 accuracy = 0.281  test_loss = 2.30112\n",
      "#Testing# 2019-06-09T21:57:00.026100: Epoch   2 Batch  164/468 accuracy = 0.406  test_loss = 2.13299\n",
      "#Testing# 2019-06-09T21:57:06.670762: Epoch   2 Batch  184/468 accuracy = 0.266  test_loss = 2.41280\n",
      "#Testing# 2019-06-09T21:57:13.324086: Epoch   2 Batch  204/468 accuracy = 0.312  test_loss = 2.22508\n",
      "#Testing# 2019-06-09T21:57:19.980308: Epoch   2 Batch  224/468 accuracy = 0.422  test_loss = 1.89322\n",
      "#Testing# 2019-06-09T21:57:26.634374: Epoch   2 Batch  244/468 accuracy = 0.344  test_loss = 2.00732\n",
      "#Testing# 2019-06-09T21:57:33.289502: Epoch   2 Batch  264/468 accuracy = 0.375  test_loss = 2.12693\n",
      "#Testing# 2019-06-09T21:57:39.944427: Epoch   2 Batch  284/468 accuracy = 0.422  test_loss = 1.99635\n",
      "#Testing# 2019-06-09T21:57:46.597194: Epoch   2 Batch  304/468 accuracy = 0.391  test_loss = 1.91756\n",
      "#Testing# 2019-06-09T21:57:53.255570: Epoch   2 Batch  324/468 accuracy = 0.281  test_loss = 2.25074\n",
      "#Testing# 2019-06-09T21:57:59.922600: Epoch   2 Batch  344/468 accuracy = 0.375  test_loss = 2.06599\n",
      "#Testing# 2019-06-09T21:58:06.569430: Epoch   2 Batch  364/468 accuracy = 0.312  test_loss = 2.09593\n",
      "#Testing# 2019-06-09T21:58:13.236733: Epoch   2 Batch  384/468 accuracy = 0.375  test_loss = 1.99374\n",
      "#Testing# 2019-06-09T21:58:19.898882: Epoch   2 Batch  404/468 accuracy = 0.375  test_loss = 2.00446\n",
      "#Testing# 2019-06-09T21:58:26.553029: Epoch   2 Batch  424/468 accuracy = 0.516  test_loss = 1.79704\n",
      "#Testing# 2019-06-09T21:58:33.205405: Epoch   2 Batch  444/468 accuracy = 0.375  test_loss = 1.99425\n",
      "#Testing# 2019-06-09T21:58:39.855270: Epoch   2 Batch  464/468 accuracy = 0.375  test_loss = 2.22709\n",
      "best loss = 2.059805317566945  acc = 0.3700921474358974\n",
      "Model saved in file: ./session/best_model.ckpt\n",
      "Training 2019-06-09T21:58:56.190838: Epoch   3 Batch   15/1875   train_loss = 1.91783\n",
      "Training 2019-06-09T21:59:15.133220: Epoch   3 Batch   35/1875   train_loss = 1.99916\n",
      "Training 2019-06-09T21:59:33.987203: Epoch   3 Batch   55/1875   train_loss = 1.93110\n",
      "Training 2019-06-09T21:59:52.963413: Epoch   3 Batch   75/1875   train_loss = 1.86847\n",
      "Training 2019-06-09T22:00:11.806460: Epoch   3 Batch   95/1875   train_loss = 1.80707\n",
      "Training 2019-06-09T22:00:30.710970: Epoch   3 Batch  115/1875   train_loss = 1.87334\n",
      "Training 2019-06-09T22:00:49.601798: Epoch   3 Batch  135/1875   train_loss = 2.19631\n",
      "Training 2019-06-09T22:01:08.534643: Epoch   3 Batch  155/1875   train_loss = 1.79054\n",
      "Training 2019-06-09T22:01:27.436367: Epoch   3 Batch  175/1875   train_loss = 1.73288\n",
      "Training 2019-06-09T22:01:46.312622: Epoch   3 Batch  195/1875   train_loss = 2.09183\n",
      "Training 2019-06-09T22:02:05.205387: Epoch   3 Batch  215/1875   train_loss = 2.13378\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training 2019-06-09T22:02:24.197711: Epoch   3 Batch  235/1875   train_loss = 2.25501\n",
      "Training 2019-06-09T22:02:43.019628: Epoch   3 Batch  255/1875   train_loss = 2.01086\n",
      "Training 2019-06-09T22:03:02.628389: Epoch   3 Batch  275/1875   train_loss = 2.19400\n",
      "Training 2019-06-09T22:03:22.102097: Epoch   3 Batch  295/1875   train_loss = 2.02915\n",
      "Training 2019-06-09T22:03:41.072676: Epoch   3 Batch  315/1875   train_loss = 2.30974\n",
      "Training 2019-06-09T22:03:59.933680: Epoch   3 Batch  335/1875   train_loss = 1.80954\n",
      "Training 2019-06-09T22:04:18.837440: Epoch   3 Batch  355/1875   train_loss = 1.80072\n",
      "Training 2019-06-09T22:04:37.707231: Epoch   3 Batch  375/1875   train_loss = 1.89397\n",
      "Training 2019-06-09T22:04:56.598497: Epoch   3 Batch  395/1875   train_loss = 2.12179\n",
      "Training 2019-06-09T22:05:15.539937: Epoch   3 Batch  415/1875   train_loss = 2.24740\n",
      "Training 2019-06-09T22:05:34.382515: Epoch   3 Batch  435/1875   train_loss = 1.94045\n",
      "Training 2019-06-09T22:05:53.174022: Epoch   3 Batch  455/1875   train_loss = 1.78117\n",
      "Training 2019-06-09T22:06:12.148317: Epoch   3 Batch  475/1875   train_loss = 1.91758\n",
      "Training 2019-06-09T22:06:31.026463: Epoch   3 Batch  495/1875   train_loss = 2.07354\n",
      "Training 2019-06-09T22:06:49.852528: Epoch   3 Batch  515/1875   train_loss = 1.89065\n",
      "Training 2019-06-09T22:07:08.711301: Epoch   3 Batch  535/1875   train_loss = 1.93433\n",
      "Training 2019-06-09T22:07:27.587772: Epoch   3 Batch  555/1875   train_loss = 2.14370\n",
      "Training 2019-06-09T22:07:46.410668: Epoch   3 Batch  575/1875   train_loss = 1.79814\n",
      "Training 2019-06-09T22:08:05.329453: Epoch   3 Batch  595/1875   train_loss = 2.09039\n",
      "Training 2019-06-09T22:08:24.953516: Epoch   3 Batch  615/1875   train_loss = 1.84550\n",
      "Training 2019-06-09T22:08:43.937895: Epoch   3 Batch  635/1875   train_loss = 1.92223\n",
      "Training 2019-06-09T22:09:02.762092: Epoch   3 Batch  655/1875   train_loss = 1.73876\n",
      "Training 2019-06-09T22:09:21.611302: Epoch   3 Batch  675/1875   train_loss = 1.90745\n",
      "Training 2019-06-09T22:09:40.461749: Epoch   3 Batch  695/1875   train_loss = 1.92387\n",
      "Training 2019-06-09T22:09:59.372538: Epoch   3 Batch  715/1875   train_loss = 1.76679\n",
      "Training 2019-06-09T22:10:18.210084: Epoch   3 Batch  735/1875   train_loss = 2.08153\n",
      "Training 2019-06-09T22:10:37.097939: Epoch   3 Batch  755/1875   train_loss = 1.69017\n",
      "Training 2019-06-09T22:10:55.890515: Epoch   3 Batch  775/1875   train_loss = 1.76737\n",
      "Training 2019-06-09T22:11:14.747620: Epoch   3 Batch  795/1875   train_loss = 2.01559\n",
      "Training 2019-06-09T22:11:33.607062: Epoch   3 Batch  815/1875   train_loss = 1.97026\n",
      "Training 2019-06-09T22:11:52.448912: Epoch   3 Batch  835/1875   train_loss = 1.98400\n",
      "Training 2019-06-09T22:12:11.319294: Epoch   3 Batch  855/1875   train_loss = 2.06102\n",
      "Training 2019-06-09T22:12:30.234880: Epoch   3 Batch  875/1875   train_loss = 2.02539\n",
      "Training 2019-06-09T22:12:49.009645: Epoch   3 Batch  895/1875   train_loss = 2.19324\n",
      "Training 2019-06-09T22:13:07.999984: Epoch   3 Batch  915/1875   train_loss = 2.04451\n",
      "Training 2019-06-09T22:13:26.925975: Epoch   3 Batch  935/1875   train_loss = 2.07593\n",
      "Training 2019-06-09T22:13:45.756553: Epoch   3 Batch  955/1875   train_loss = 1.71689\n",
      "Training 2019-06-09T22:14:04.593692: Epoch   3 Batch  975/1875   train_loss = 1.65418\n",
      "Training 2019-06-09T22:14:23.418506: Epoch   3 Batch  995/1875   train_loss = 1.93602\n",
      "Training 2019-06-09T22:14:42.266266: Epoch   3 Batch 1015/1875   train_loss = 1.99594\n",
      "Training 2019-06-09T22:15:01.196226: Epoch   3 Batch 1035/1875   train_loss = 1.48836\n",
      "Training 2019-06-09T22:15:20.076499: Epoch   3 Batch 1055/1875   train_loss = 2.32525\n",
      "Training 2019-06-09T22:15:39.003534: Epoch   3 Batch 1075/1875   train_loss = 2.29623\n",
      "Training 2019-06-09T22:15:57.909481: Epoch   3 Batch 1095/1875   train_loss = 2.06455\n",
      "Training 2019-06-09T22:16:16.759977: Epoch   3 Batch 1115/1875   train_loss = 1.88504\n",
      "Training 2019-06-09T22:16:35.630597: Epoch   3 Batch 1135/1875   train_loss = 1.94385\n",
      "Training 2019-06-09T22:16:54.459672: Epoch   3 Batch 1155/1875   train_loss = 1.87048\n",
      "Training 2019-06-09T22:17:13.295839: Epoch   3 Batch 1175/1875   train_loss = 2.09377\n",
      "Training 2019-06-09T22:17:32.122722: Epoch   3 Batch 1195/1875   train_loss = 1.73863\n",
      "Training 2019-06-09T22:17:50.960673: Epoch   3 Batch 1215/1875   train_loss = 2.06057\n",
      "Training 2019-06-09T22:18:09.817964: Epoch   3 Batch 1235/1875   train_loss = 1.86195\n",
      "Training 2019-06-09T22:18:28.668415: Epoch   3 Batch 1255/1875   train_loss = 2.17679\n",
      "Training 2019-06-09T22:18:48.742697: Epoch   3 Batch 1275/1875   train_loss = 1.96873\n",
      "Training 2019-06-09T22:19:07.868547: Epoch   3 Batch 1295/1875   train_loss = 1.85594\n",
      "Training 2019-06-09T22:19:26.785970: Epoch   3 Batch 1315/1875   train_loss = 1.89489\n",
      "Training 2019-06-09T22:19:45.693887: Epoch   3 Batch 1335/1875   train_loss = 1.95333\n",
      "Training 2019-06-09T22:20:04.582961: Epoch   3 Batch 1355/1875   train_loss = 1.82033\n",
      "Training 2019-06-09T22:20:23.459264: Epoch   3 Batch 1375/1875   train_loss = 1.80688\n",
      "Training 2019-06-09T22:20:42.303994: Epoch   3 Batch 1395/1875   train_loss = 1.94212\n",
      "Training 2019-06-09T22:21:01.256975: Epoch   3 Batch 1415/1875   train_loss = 1.82986\n",
      "Training 2019-06-09T22:21:20.145371: Epoch   3 Batch 1435/1875   train_loss = 1.84572\n",
      "Training 2019-06-09T22:21:38.979847: Epoch   3 Batch 1455/1875   train_loss = 1.92136\n",
      "Training 2019-06-09T22:21:57.851441: Epoch   3 Batch 1475/1875   train_loss = 1.94069\n",
      "Training 2019-06-09T22:22:16.667094: Epoch   3 Batch 1495/1875   train_loss = 1.78142\n",
      "Training 2019-06-09T22:22:35.575100: Epoch   3 Batch 1515/1875   train_loss = 1.79241\n",
      "Training 2019-06-09T22:22:54.417007: Epoch   3 Batch 1535/1875   train_loss = 2.05233\n",
      "Training 2019-06-09T22:23:13.426606: Epoch   3 Batch 1555/1875   train_loss = 2.30077\n",
      "Training 2019-06-09T22:23:32.298472: Epoch   3 Batch 1575/1875   train_loss = 1.98656\n",
      "Training 2019-06-09T22:23:51.167741: Epoch   3 Batch 1595/1875   train_loss = 1.83309\n",
      "Training 2019-06-09T22:24:11.047431: Epoch   3 Batch 1615/1875   train_loss = 1.89693\n",
      "Training 2019-06-09T22:24:30.015670: Epoch   3 Batch 1635/1875   train_loss = 1.71829\n",
      "Training 2019-06-09T22:24:48.881008: Epoch   3 Batch 1655/1875   train_loss = 1.88390\n",
      "Training 2019-06-09T22:25:07.827967: Epoch   3 Batch 1675/1875   train_loss = 1.62183\n",
      "Training 2019-06-09T22:25:26.712828: Epoch   3 Batch 1695/1875   train_loss = 1.89190\n",
      "Training 2019-06-09T22:25:45.589503: Epoch   3 Batch 1715/1875   train_loss = 2.16317\n",
      "Training 2019-06-09T22:26:04.433117: Epoch   3 Batch 1735/1875   train_loss = 1.74055\n",
      "Training 2019-06-09T22:26:23.357457: Epoch   3 Batch 1755/1875   train_loss = 1.61261\n",
      "Training 2019-06-09T22:26:42.162573: Epoch   3 Batch 1775/1875   train_loss = 1.82241\n",
      "Training 2019-06-09T22:27:01.110505: Epoch   3 Batch 1795/1875   train_loss = 2.08949\n",
      "Training 2019-06-09T22:27:20.005551: Epoch   3 Batch 1815/1875   train_loss = 1.79015\n",
      "Training 2019-06-09T22:27:38.928115: Epoch   3 Batch 1835/1875   train_loss = 2.04451\n",
      "Training 2019-06-09T22:27:57.891614: Epoch   3 Batch 1855/1875   train_loss = 1.92878\n",
      "#Testing# 2019-06-09T22:28:21.489344: Epoch   3 Batch   16/468 accuracy = 0.438  test_loss = 1.93022\n",
      "#Testing# 2019-06-09T22:28:28.133676: Epoch   3 Batch   36/468 accuracy = 0.484  test_loss = 1.72057\n",
      "#Testing# 2019-06-09T22:28:34.784299: Epoch   3 Batch   56/468 accuracy = 0.469  test_loss = 1.91806\n",
      "#Testing# 2019-06-09T22:28:41.439148: Epoch   3 Batch   76/468 accuracy = 0.469  test_loss = 1.83727\n",
      "#Testing# 2019-06-09T22:28:48.073486: Epoch   3 Batch   96/468 accuracy = 0.406  test_loss = 1.81415\n",
      "#Testing# 2019-06-09T22:28:54.734913: Epoch   3 Batch  116/468 accuracy = 0.453  test_loss = 1.81819\n",
      "#Testing# 2019-06-09T22:29:01.386678: Epoch   3 Batch  136/468 accuracy = 0.391  test_loss = 1.87148\n",
      "#Testing# 2019-06-09T22:29:08.027729: Epoch   3 Batch  156/468 accuracy = 0.406  test_loss = 1.92471\n",
      "#Testing# 2019-06-09T22:29:14.684485: Epoch   3 Batch  176/468 accuracy = 0.328  test_loss = 2.33162\n",
      "#Testing# 2019-06-09T22:29:21.335269: Epoch   3 Batch  196/468 accuracy = 0.328  test_loss = 2.05526\n",
      "#Testing# 2019-06-09T22:29:27.980509: Epoch   3 Batch  216/468 accuracy = 0.406  test_loss = 1.96777\n",
      "#Testing# 2019-06-09T22:29:34.627157: Epoch   3 Batch  236/468 accuracy = 0.484  test_loss = 2.03164\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Testing# 2019-06-09T22:29:41.287901: Epoch   3 Batch  256/468 accuracy = 0.438  test_loss = 1.82348\n",
      "#Testing# 2019-06-09T22:29:47.938062: Epoch   3 Batch  276/468 accuracy = 0.406  test_loss = 2.05334\n",
      "#Testing# 2019-06-09T22:29:54.589912: Epoch   3 Batch  296/468 accuracy = 0.391  test_loss = 1.93162\n",
      "#Testing# 2019-06-09T22:30:01.237163: Epoch   3 Batch  316/468 accuracy = 0.406  test_loss = 1.83407\n",
      "#Testing# 2019-06-09T22:30:07.884956: Epoch   3 Batch  336/468 accuracy = 0.359  test_loss = 2.03912\n",
      "#Testing# 2019-06-09T22:30:14.540767: Epoch   3 Batch  356/468 accuracy = 0.531  test_loss = 1.77512\n",
      "#Testing# 2019-06-09T22:30:21.185006: Epoch   3 Batch  376/468 accuracy = 0.391  test_loss = 1.92428\n",
      "#Testing# 2019-06-09T22:30:27.847815: Epoch   3 Batch  396/468 accuracy = 0.375  test_loss = 1.93500\n",
      "#Testing# 2019-06-09T22:30:34.501514: Epoch   3 Batch  416/468 accuracy = 0.281  test_loss = 2.16452\n",
      "#Testing# 2019-06-09T22:30:41.142953: Epoch   3 Batch  436/468 accuracy = 0.438  test_loss = 1.90072\n",
      "#Testing# 2019-06-09T22:30:47.788704: Epoch   3 Batch  456/468 accuracy = 0.234  test_loss = 2.34566\n",
      "best loss = 1.958253463364055  acc = 0.4008079594017094\n",
      "Model saved in file: ./session/best_model.ckpt\n",
      "Training 2019-06-09T22:30:52.654261: Epoch   4 Batch    0/1875   train_loss = 2.03831\n",
      "Training 2019-06-09T22:31:11.529361: Epoch   4 Batch   20/1875   train_loss = 2.16070\n",
      "Training 2019-06-09T22:31:30.483258: Epoch   4 Batch   40/1875   train_loss = 1.80822\n",
      "Training 2019-06-09T22:31:49.361535: Epoch   4 Batch   60/1875   train_loss = 1.90060\n",
      "Training 2019-06-09T22:32:08.242850: Epoch   4 Batch   80/1875   train_loss = 2.03202\n",
      "Training 2019-06-09T22:32:27.119570: Epoch   4 Batch  100/1875   train_loss = 1.98465\n",
      "Training 2019-06-09T22:32:46.025666: Epoch   4 Batch  120/1875   train_loss = 1.86419\n",
      "Training 2019-06-09T22:33:04.858122: Epoch   4 Batch  140/1875   train_loss = 1.82064\n",
      "Training 2019-06-09T22:33:23.767406: Epoch   4 Batch  160/1875   train_loss = 1.63187\n",
      "Training 2019-06-09T22:33:42.674070: Epoch   4 Batch  180/1875   train_loss = 1.91043\n",
      "Training 2019-06-09T22:34:01.619839: Epoch   4 Batch  200/1875   train_loss = 1.78630\n",
      "Training 2019-06-09T22:34:20.450918: Epoch   4 Batch  220/1875   train_loss = 1.87755\n",
      "Training 2019-06-09T22:34:39.303019: Epoch   4 Batch  240/1875   train_loss = 1.83286\n",
      "Training 2019-06-09T22:34:58.930370: Epoch   4 Batch  260/1875   train_loss = 1.73551\n",
      "Training 2019-06-09T22:35:18.192474: Epoch   4 Batch  280/1875   train_loss = 1.78032\n",
      "Training 2019-06-09T22:35:37.217686: Epoch   4 Batch  300/1875   train_loss = 1.73118\n",
      "Training 2019-06-09T22:35:56.082170: Epoch   4 Batch  320/1875   train_loss = 1.78312\n",
      "Training 2019-06-09T22:36:14.987909: Epoch   4 Batch  340/1875   train_loss = 1.91861\n",
      "Training 2019-06-09T22:36:33.846058: Epoch   4 Batch  360/1875   train_loss = 1.98843\n",
      "Training 2019-06-09T22:36:52.804195: Epoch   4 Batch  380/1875   train_loss = 2.05322\n",
      "Training 2019-06-09T22:37:11.751469: Epoch   4 Batch  400/1875   train_loss = 1.73227\n",
      "Training 2019-06-09T22:37:30.682681: Epoch   4 Batch  420/1875   train_loss = 2.01386\n",
      "Training 2019-06-09T22:37:49.588992: Epoch   4 Batch  440/1875   train_loss = 1.57703\n",
      "Training 2019-06-09T22:38:08.490136: Epoch   4 Batch  460/1875   train_loss = 2.04826\n",
      "Training 2019-06-09T22:38:27.408169: Epoch   4 Batch  480/1875   train_loss = 2.14310\n",
      "Training 2019-06-09T22:38:46.300078: Epoch   4 Batch  500/1875   train_loss = 1.68962\n",
      "Training 2019-06-09T22:39:05.143185: Epoch   4 Batch  520/1875   train_loss = 1.83985\n",
      "Training 2019-06-09T22:39:24.029659: Epoch   4 Batch  540/1875   train_loss = 1.67824\n",
      "Training 2019-06-09T22:39:42.832037: Epoch   4 Batch  560/1875   train_loss = 1.71841\n",
      "Training 2019-06-09T22:40:01.697297: Epoch   4 Batch  580/1875   train_loss = 1.83708\n",
      "Training 2019-06-09T22:40:21.050230: Epoch   4 Batch  600/1875   train_loss = 1.69876\n",
      "Training 2019-06-09T22:40:40.640279: Epoch   4 Batch  620/1875   train_loss = 1.92831\n",
      "Training 2019-06-09T22:40:59.576264: Epoch   4 Batch  640/1875   train_loss = 1.76318\n",
      "Training 2019-06-09T22:41:18.418177: Epoch   4 Batch  660/1875   train_loss = 2.16945\n",
      "Training 2019-06-09T22:41:37.316928: Epoch   4 Batch  680/1875   train_loss = 1.71791\n",
      "Training 2019-06-09T22:41:56.146320: Epoch   4 Batch  700/1875   train_loss = 1.68853\n",
      "Training 2019-06-09T22:42:15.083595: Epoch   4 Batch  720/1875   train_loss = 1.67113\n",
      "Training 2019-06-09T22:42:34.035479: Epoch   4 Batch  740/1875   train_loss = 1.86159\n",
      "Training 2019-06-09T22:42:52.923993: Epoch   4 Batch  760/1875   train_loss = 1.63933\n",
      "Training 2019-06-09T22:43:11.921929: Epoch   4 Batch  780/1875   train_loss = 1.95795\n",
      "Training 2019-06-09T22:43:30.858316: Epoch   4 Batch  800/1875   train_loss = 1.88500\n",
      "Training 2019-06-09T22:43:49.713551: Epoch   4 Batch  820/1875   train_loss = 1.70058\n",
      "Training 2019-06-09T22:44:08.608998: Epoch   4 Batch  840/1875   train_loss = 1.71121\n",
      "Training 2019-06-09T22:44:27.508922: Epoch   4 Batch  860/1875   train_loss = 1.90049\n",
      "Training 2019-06-09T22:44:46.377097: Epoch   4 Batch  880/1875   train_loss = 1.83563\n",
      "Training 2019-06-09T22:45:05.267418: Epoch   4 Batch  900/1875   train_loss = 1.67382\n",
      "Training 2019-06-09T22:45:24.166191: Epoch   4 Batch  920/1875   train_loss = 1.97723\n",
      "Training 2019-06-09T22:45:43.041932: Epoch   4 Batch  940/1875   train_loss = 1.75266\n",
      "Training 2019-06-09T22:46:02.056962: Epoch   4 Batch  960/1875   train_loss = 1.65444\n",
      "Training 2019-06-09T22:46:20.896512: Epoch   4 Batch  980/1875   train_loss = 1.92964\n",
      "Training 2019-06-09T22:46:40.678045: Epoch   4 Batch 1000/1875   train_loss = 1.68097\n",
      "Training 2019-06-09T22:46:59.611640: Epoch   4 Batch 1020/1875   train_loss = 1.81917\n",
      "Training 2019-06-09T22:47:18.493434: Epoch   4 Batch 1040/1875   train_loss = 1.77757\n",
      "Training 2019-06-09T22:47:37.432890: Epoch   4 Batch 1060/1875   train_loss = 1.95890\n",
      "Training 2019-06-09T22:47:56.312301: Epoch   4 Batch 1080/1875   train_loss = 1.70789\n",
      "Training 2019-06-09T22:48:15.211912: Epoch   4 Batch 1100/1875   train_loss = 1.59921\n",
      "Training 2019-06-09T22:48:34.108789: Epoch   4 Batch 1120/1875   train_loss = 1.73211\n",
      "Training 2019-06-09T22:48:52.969772: Epoch   4 Batch 1140/1875   train_loss = 1.98654\n",
      "Training 2019-06-09T22:49:12.528672: Epoch   4 Batch 1160/1875   train_loss = 1.81648\n",
      "Training 2019-06-09T22:49:36.127977: Epoch   4 Batch 1180/1875   train_loss = 1.78107\n",
      "Training 2019-06-09T22:49:59.320747: Epoch   4 Batch 1200/1875   train_loss = 1.65382\n",
      "Training 2019-06-09T22:50:19.227249: Epoch   4 Batch 1220/1875   train_loss = 1.68290\n",
      "Training 2019-06-09T22:50:42.227109: Epoch   4 Batch 1240/1875   train_loss = 1.84485\n",
      "Training 2019-06-09T22:51:03.395393: Epoch   4 Batch 1260/1875   train_loss = 1.80888\n",
      "Training 2019-06-09T22:51:23.022046: Epoch   4 Batch 1280/1875   train_loss = 1.80917\n",
      "Training 2019-06-09T22:51:45.068916: Epoch   4 Batch 1300/1875   train_loss = 1.50928\n",
      "Training 2019-06-09T22:52:04.973415: Epoch   4 Batch 1320/1875   train_loss = 2.07361\n",
      "Training 2019-06-09T22:52:24.714751: Epoch   4 Batch 1340/1875   train_loss = 1.71857\n",
      "Training 2019-06-09T22:52:43.800919: Epoch   4 Batch 1360/1875   train_loss = 1.62398\n",
      "Training 2019-06-09T22:53:02.877725: Epoch   4 Batch 1380/1875   train_loss = 1.80707\n",
      "Training 2019-06-09T22:53:22.094208: Epoch   4 Batch 1400/1875   train_loss = 2.08484\n",
      "Training 2019-06-09T22:53:41.168083: Epoch   4 Batch 1420/1875   train_loss = 1.71400\n",
      "Training 2019-06-09T22:54:00.252411: Epoch   4 Batch 1440/1875   train_loss = 1.63851\n",
      "Training 2019-06-09T22:54:19.356168: Epoch   4 Batch 1460/1875   train_loss = 1.80255\n",
      "Training 2019-06-09T22:54:38.416493: Epoch   4 Batch 1480/1875   train_loss = 1.70522\n",
      "Training 2019-06-09T22:54:57.506948: Epoch   4 Batch 1500/1875   train_loss = 1.84904\n",
      "Training 2019-06-09T22:55:16.575145: Epoch   4 Batch 1520/1875   train_loss = 1.50934\n",
      "Training 2019-06-09T22:55:35.710694: Epoch   4 Batch 1540/1875   train_loss = 2.02387\n",
      "Training 2019-06-09T22:55:54.785233: Epoch   4 Batch 1560/1875   train_loss = 1.69920\n",
      "Training 2019-06-09T22:56:13.869047: Epoch   4 Batch 1580/1875   train_loss = 1.95369\n",
      "Training 2019-06-09T22:56:32.893204: Epoch   4 Batch 1600/1875   train_loss = 1.87572\n",
      "Training 2019-06-09T22:56:51.985366: Epoch   4 Batch 1620/1875   train_loss = 1.87879\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training 2019-06-09T22:57:10.999107: Epoch   4 Batch 1640/1875   train_loss = 1.45643\n",
      "Training 2019-06-09T22:57:30.154800: Epoch   4 Batch 1660/1875   train_loss = 1.55792\n",
      "Training 2019-06-09T22:57:49.240498: Epoch   4 Batch 1680/1875   train_loss = 1.86889\n",
      "Training 2019-06-09T22:58:08.341170: Epoch   4 Batch 1700/1875   train_loss = 1.63730\n",
      "Training 2019-06-09T22:58:27.396891: Epoch   4 Batch 1720/1875   train_loss = 1.78511\n",
      "Training 2019-06-09T22:58:46.478558: Epoch   4 Batch 1740/1875   train_loss = 1.58699\n",
      "Training 2019-06-09T22:59:06.888858: Epoch   4 Batch 1760/1875   train_loss = 1.76029\n",
      "Training 2019-06-09T22:59:26.232135: Epoch   4 Batch 1780/1875   train_loss = 2.02823\n",
      "Training 2019-06-09T22:59:45.274038: Epoch   4 Batch 1800/1875   train_loss = 1.87375\n",
      "Training 2019-06-09T23:00:04.345088: Epoch   4 Batch 1820/1875   train_loss = 1.62470\n",
      "Training 2019-06-09T23:00:23.435480: Epoch   4 Batch 1840/1875   train_loss = 1.69743\n",
      "Training 2019-06-09T23:00:42.487954: Epoch   4 Batch 1860/1875   train_loss = 1.73896\n",
      "#Testing# 2019-06-09T23:00:58.863127: Epoch   4 Batch    8/468 accuracy = 0.469  test_loss = 2.03604\n",
      "#Testing# 2019-06-09T23:01:05.528318: Epoch   4 Batch   28/468 accuracy = 0.469  test_loss = 1.96811\n",
      "#Testing# 2019-06-09T23:01:12.203563: Epoch   4 Batch   48/468 accuracy = 0.406  test_loss = 2.04902\n",
      "#Testing# 2019-06-09T23:01:18.931336: Epoch   4 Batch   68/468 accuracy = 0.375  test_loss = 1.97741\n",
      "#Testing# 2019-06-09T23:01:25.605773: Epoch   4 Batch   88/468 accuracy = 0.391  test_loss = 1.88576\n",
      "#Testing# 2019-06-09T23:01:32.295191: Epoch   4 Batch  108/468 accuracy = 0.453  test_loss = 1.94737\n",
      "#Testing# 2019-06-09T23:01:38.965660: Epoch   4 Batch  128/468 accuracy = 0.312  test_loss = 2.08563\n",
      "#Testing# 2019-06-09T23:01:45.644409: Epoch   4 Batch  148/468 accuracy = 0.406  test_loss = 1.82753\n",
      "#Testing# 2019-06-09T23:01:52.318701: Epoch   4 Batch  168/468 accuracy = 0.422  test_loss = 1.92515\n",
      "#Testing# 2019-06-09T23:01:58.995645: Epoch   4 Batch  188/468 accuracy = 0.359  test_loss = 2.26016\n",
      "#Testing# 2019-06-09T23:02:05.996444: Epoch   4 Batch  208/468 accuracy = 0.375  test_loss = 2.01934\n",
      "#Testing# 2019-06-09T23:02:12.682433: Epoch   4 Batch  228/468 accuracy = 0.391  test_loss = 2.00958\n",
      "#Testing# 2019-06-09T23:02:19.392602: Epoch   4 Batch  248/468 accuracy = 0.453  test_loss = 1.78911\n",
      "#Testing# 2019-06-09T23:02:26.043595: Epoch   4 Batch  268/468 accuracy = 0.375  test_loss = 2.02302\n",
      "#Testing# 2019-06-09T23:02:32.718293: Epoch   4 Batch  288/468 accuracy = 0.453  test_loss = 2.02668\n",
      "#Testing# 2019-06-09T23:02:39.379866: Epoch   4 Batch  308/468 accuracy = 0.406  test_loss = 2.08591\n",
      "#Testing# 2019-06-09T23:02:46.047107: Epoch   4 Batch  328/468 accuracy = 0.375  test_loss = 2.06005\n",
      "#Testing# 2019-06-09T23:02:52.705172: Epoch   4 Batch  348/468 accuracy = 0.359  test_loss = 1.99887\n",
      "#Testing# 2019-06-09T23:02:59.351969: Epoch   4 Batch  368/468 accuracy = 0.391  test_loss = 1.79789\n",
      "#Testing# 2019-06-09T23:03:06.034405: Epoch   4 Batch  388/468 accuracy = 0.469  test_loss = 1.89008\n",
      "#Testing# 2019-06-09T23:03:12.685478: Epoch   4 Batch  408/468 accuracy = 0.312  test_loss = 2.09964\n",
      "#Testing# 2019-06-09T23:03:19.379099: Epoch   4 Batch  428/468 accuracy = 0.484  test_loss = 1.72707\n",
      "#Testing# 2019-06-09T23:03:26.031733: Epoch   4 Batch  448/468 accuracy = 0.484  test_loss = 1.76846\n",
      "best loss = 1.8720741564901466  acc = 0.42715010683760685\n",
      "Model saved in file: ./session/best_model.ckpt\n"
     ]
    }
   ],
   "source": [
    "epochs = 5\n",
    "net = network()\n",
    "total_batch_size = 64\n",
    "\n",
    "best_loss = 9999\n",
    "\n",
    "for ii in range(epochs):\n",
    "    train_batches = get_batches(train_xs, train_ys, total_batch_size)\n",
    "    test_batches = get_batches(valid_xs, valid_ys, total_batch_size)\n",
    "    batch_num = (len(train_xs) // total_batch_size)\n",
    "    for batch_i in range(batch_num):\n",
    "        x, y = next(train_batches)\n",
    "        net.training(x, y, ii * (batch_num) + batch_i, ii, batch_i, batch_num)\n",
    "        \n",
    "    batch_num = (len(valid_xs) // total_batch_size)\n",
    "    test_loss = 0.0\n",
    "    test_acc = 0.0\n",
    "    for batch_i  in range(batch_num):\n",
    "        x, y = next(test_batches)\n",
    "        acc, loss = net.testing(x, y, ii * (batch_num) + batch_i, ii, batch_i, batch_num)\n",
    "        test_loss = test_loss + loss\n",
    "        test_acc = test_acc + acc\n",
    "        \n",
    "    test_acc = test_acc / batch_num\n",
    "    test_loss = test_loss / batch_num\n",
    "    if test_loss < best_loss:\n",
    "        best_loss = test_loss\n",
    "        print(\"best loss = {}  acc = {}\".format(best_loss, test_acc))\n",
    "        net.save()\n",
    "    else:\n",
    "        print(\"test loss = {}  acc = {}\".format(test_loss, test_acc))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conv1.shape =  (?, 28, 28, 100)\n",
      "conv2.shape =  (?, 12, 12, 150)\n",
      "max_pool conv2.shape =  (?, 6, 6, 150)\n",
      "conv3.shape =  (?, 6, 6, 250)\n",
      "max_pool conv3.shape =  (?, 3, 3, 250)\n",
      "fc0.shape =  (?, 2250)\n",
      "WARNING:tensorflow:From /Applications/anaconda/envs/tf1.13.1/lib/python3.6/site-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use standard file APIs to check for files with this prefix.\n",
      "INFO:tensorflow:Restoring parameters from ./session/best_model.ckpt\n",
      "Successfully loaded: ./session/best_model.ckpt\n",
      "#Testing# 2019-06-10T09:10:19.299590: Epoch   0 Batch    0/156 accuracy = 0.500  test_loss = 1.93074\n",
      "#Testing# 2019-06-10T09:10:26.021611: Epoch   0 Batch   20/156 accuracy = 0.344  test_loss = 1.95703\n",
      "#Testing# 2019-06-10T09:10:32.692765: Epoch   0 Batch   40/156 accuracy = 0.344  test_loss = 2.03040\n",
      "#Testing# 2019-06-10T09:10:39.882692: Epoch   0 Batch   60/156 accuracy = 0.344  test_loss = 2.07493\n",
      "#Testing# 2019-06-10T09:10:47.132907: Epoch   0 Batch   80/156 accuracy = 0.438  test_loss = 1.96972\n",
      "#Testing# 2019-06-10T09:10:53.803962: Epoch   0 Batch  100/156 accuracy = 0.375  test_loss = 2.02401\n",
      "#Testing# 2019-06-10T09:11:00.467231: Epoch   0 Batch  120/156 accuracy = 0.391  test_loss = 2.00708\n",
      "#Testing# 2019-06-10T09:11:07.136837: Epoch   0 Batch  140/156 accuracy = 0.328  test_loss = 2.08144\n",
      "test loss = 1.9895841059012291  acc = 0.3975360576923077\n"
     ]
    }
   ],
   "source": [
    "net = network()\n",
    "\n",
    "test_batches = get_batches(test_set, y_test, total_batch_size)\n",
    "batch_num = (len(test_set) // total_batch_size)\n",
    "test_loss = 0.0\n",
    "test_acc = 0.0\n",
    "for batch_i  in range(batch_num):\n",
    "    x, y = next(test_batches)\n",
    "    acc, loss = net.testing(x, y, 0 * (batch_num) + batch_i, 0, batch_i, batch_num)\n",
    "    test_loss = test_loss + loss\n",
    "    test_acc = test_acc + acc\n",
    "        \n",
    "test_acc = test_acc / batch_num\n",
    "test_loss = test_loss / batch_num\n",
    "print(\"test loss = {}  acc = {}\".format(test_loss, test_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
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  "widgets": {
   "state": {},
   "version": "1.1.2"
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 },
 "nbformat": 4,
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